[ aws . rekognition ]

create-project-version

Description

Creates a new version of a model and begins training. Models are managed as part of an Amazon Rekognition Custom Labels project. You can specify one training dataset and one testing dataset. The response from CreateProjectVersion is an Amazon Resource Name (ARN) for the version of the model.

Training takes a while to complete. You can get the current status by calling DescribeProjectVersions .

Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model.

After evaluating the model, you start the model by calling StartProjectVersion .

This operation requires permissions to perform the rekognition:CreateProjectVersion action.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  create-project-version
--project-arn <value>
--version-name <value>
--output-config <value>
--training-data <value>
--testing-data <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--project-arn (string)

The ARN of the Amazon Rekognition Custom Labels project that manages the model that you want to train.

--version-name (string)

A name for the version of the model. This value must be unique.

--output-config (structure)

The Amazon S3 location to store the results of training.

S3Bucket -> (string)

The S3 bucket where training output is placed.

S3KeyPrefix -> (string)

The prefix applied to the training output files.

Shorthand Syntax:

S3Bucket=string,S3KeyPrefix=string

JSON Syntax:

{
  "S3Bucket": "string",
  "S3KeyPrefix": "string"
}

--training-data (structure)

The dataset to use for training.

Assets -> (list)

A Sagemaker GroundTruth manifest file that contains the training images (assets).

(structure)

Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.

GroundTruthManifest -> (structure)

The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see Resource-Based Policies in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

JSON Syntax:

{
  "Assets": [
    {
      "GroundTruthManifest": {
        "S3Object": {
          "Bucket": "string",
          "Name": "string",
          "Version": "string"
        }
      }
    }
    ...
  ]
}

--testing-data (structure)

The dataset to use for testing.

Assets -> (list)

The assets used for testing.

(structure)

Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.

GroundTruthManifest -> (structure)

The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see Resource-Based Policies in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

AutoCreate -> (boolean)

If specified, Amazon Rekognition Custom Labels creates a testing dataset with an 80/20 split of the training dataset.

JSON Syntax:

{
  "Assets": [
    {
      "GroundTruthManifest": {
        "S3Object": {
          "Bucket": "string",
          "Name": "string",
          "Version": "string"
        }
      }
    }
    ...
  ],
  "AutoCreate": true|false
}

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See ‘aws help’ for descriptions of global parameters.

Output

ProjectVersionArn -> (string)

The ARN of the model version that was created. Use DescribeProjectVersion to get the current status of the training operation.